{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:2FFIYD4BN7SSGFCWY45D3OAQXG","short_pith_number":"pith:2FFIYD4B","schema_version":"1.0","canonical_sha256":"d14a8c0f816fe5231456c73a3db810b9a8154ab1beb4433d74ce7fb7776fd648","source":{"kind":"arxiv","id":"2304.13844","version":1},"attestation_state":"computed","paper":{"title":"GazeSAM: What You See is What You Segment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Armstrong Aboah, Bin Wang, Ulas Bagci, Zheyuan Zhang","submitted_at":"2023-04-26T22:18:29Z","abstract_excerpt":"This study investigates the potential of eye-tracking technology and the Segment Anything Model (SAM) to design a collaborative human-computer interaction system that automates medical image segmentation. We present the \\textbf{GazeSAM} system to enable radiologists to collect segmentation masks by simply looking at the region of interest during image diagnosis. The proposed system tracks radiologists' eye movement and utilizes the eye-gaze data as the input prompt for SAM, which automatically generates the segmentation mask in real time. This study is the first work to leverage the power of e"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2304.13844","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-26T22:18:29Z","cross_cats_sorted":[],"title_canon_sha256":"eb073a113e1065a77539d9d1d01fbfeabc8e33af2bb7619b74eec0e959912b22","abstract_canon_sha256":"3de8f9597d5743d30a6095a7ba57fb84e50d3ff39ce30c330546a42bd9727c11"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:04:57.698674Z","signature_b64":"79oL/xyBI14+xtZhl03wCXtJTve7BdOQc7gTN9IfncOjUdtF4MkoYW84AWTxeIhNxeN/ttaqzbH5eVz/TyxDAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d14a8c0f816fe5231456c73a3db810b9a8154ab1beb4433d74ce7fb7776fd648","last_reissued_at":"2026-07-05T06:04:57.698243Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:04:57.698243Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GazeSAM: What You See is What You Segment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Armstrong Aboah, Bin Wang, Ulas Bagci, Zheyuan Zhang","submitted_at":"2023-04-26T22:18:29Z","abstract_excerpt":"This study investigates the potential of eye-tracking technology and the Segment Anything Model (SAM) to design a collaborative human-computer interaction system that automates medical image segmentation. We present the \\textbf{GazeSAM} system to enable radiologists to collect segmentation masks by simply looking at the region of interest during image diagnosis. The proposed system tracks radiologists' eye movement and utilizes the eye-gaze data as the input prompt for SAM, which automatically generates the segmentation mask in real time. This study is the first work to leverage the power of e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.13844","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2304.13844/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2304.13844","created_at":"2026-07-05T06:04:57.698305+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.13844v1","created_at":"2026-07-05T06:04:57.698305+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.13844","created_at":"2026-07-05T06:04:57.698305+00:00"},{"alias_kind":"pith_short_12","alias_value":"2FFIYD4BN7SS","created_at":"2026-07-05T06:04:57.698305+00:00"},{"alias_kind":"pith_short_16","alias_value":"2FFIYD4BN7SSGFCW","created_at":"2026-07-05T06:04:57.698305+00:00"},{"alias_kind":"pith_short_8","alias_value":"2FFIYD4B","created_at":"2026-07-05T06:04:57.698305+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.09478","citing_title":"GazeLT: Visual attention-guided long-tailed disease classification in chest radiographs","ref_index":45,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2FFIYD4BN7SSGFCWY45D3OAQXG","json":"https://pith.science/pith/2FFIYD4BN7SSGFCWY45D3OAQXG.json","graph_json":"https://pith.science/api/pith-number/2FFIYD4BN7SSGFCWY45D3OAQXG/graph.json","events_json":"https://pith.science/api/pith-number/2FFIYD4BN7SSGFCWY45D3OAQXG/events.json","paper":"https://pith.science/paper/2FFIYD4B"},"agent_actions":{"view_html":"https://pith.science/pith/2FFIYD4BN7SSGFCWY45D3OAQXG","download_json":"https://pith.science/pith/2FFIYD4BN7SSGFCWY45D3OAQXG.json","view_paper":"https://pith.science/paper/2FFIYD4B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.13844&json=true","fetch_graph":"https://pith.science/api/pith-number/2FFIYD4BN7SSGFCWY45D3OAQXG/graph.json","fetch_events":"https://pith.science/api/pith-number/2FFIYD4BN7SSGFCWY45D3OAQXG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2FFIYD4BN7SSGFCWY45D3OAQXG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2FFIYD4BN7SSGFCWY45D3OAQXG/action/storage_attestation","attest_author":"https://pith.science/pith/2FFIYD4BN7SSGFCWY45D3OAQXG/action/author_attestation","sign_citation":"https://pith.science/pith/2FFIYD4BN7SSGFCWY45D3OAQXG/action/citation_signature","submit_replication":"https://pith.science/pith/2FFIYD4BN7SSGFCWY45D3OAQXG/action/replication_record"}},"created_at":"2026-07-05T06:04:57.698305+00:00","updated_at":"2026-07-05T06:04:57.698305+00:00"}